Microservice manager and optimizer

ABSTRACT

In one embodiment, an agent executed by a device detects an invocation that is made using Java reflection of a method associated with a microservice. The agent instruments the invocation of the method associated with the microservice, to capture one or more metrics regarding the microservice. The agent optimizes the invocation of the method associated with the microservice. The agent provides the one or more metrics regarding the microservice to a user interface.

TECHNICAL FIELD

The present disclosure relates generally to computer systems, and, moreparticularly, to a microservice manager and optimizer.

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses. Due to theaccompanying complexity of the infrastructure supporting the webservices, it is becoming increasingly difficult to maintain the highestlevel of service performance and user experience to keep up with theincrease in web services. For example, it can be challenging to piecetogether monitoring and logging data across disparate systems, tools,and layers in a network architecture. Moreover, even when data can beobtained, it is difficult to directly connect the chain of events andcause and effect. To this end, various application performancemanagement (APM) solutions have emerged that typically rely oninstrumentation, which is the process of inserting code into anapplication, to capture performance data.

Recently, microservices have emerged as a major paradigm shift in thecomputing world. Rather than deploying monolithic applications, manycompanies are now choosing instead to develop scalable microservicesthat break the tasks of the application into individual runtimes, tohandle the web service calls. In the context of the Java programminglanguage, there are now multiple frameworks designed to supportmicroservices. To do so, these platforms typically operate by exposingRepresentational State Transfer (REST) endpoints, which perform specificservices. Microservices also typically use runtimes that are muchsmaller in terms of memory and much more dynamic than monolithicapplications. Consequently, traditional APM solutions are not wellsuited for microservice frameworks, as their additional overhead may notbe compatible with the resource constraints found in microserviceframeworks.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for implementing the exampleapplication intelligence platform;

FIG. 5 illustrates an example computing system;

FIG. 6 illustrates an example simplified architecture for a microserviceframework with an agent;

FIG. 7 illustrates an example folder structure for an agent;

FIGS. 8A-8D illustrate examples output of a prototype implementing theis techniques herein; and

FIG. 9 illustrates an example simplified procedure for instrumenting andoptimizing a microservice-based application, in accordance with one ormore embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, an agentexecuted by a device detects an invocation that is made using Javareflection of a method associated with a microservice. The agentinstruments the invocation of the method associated with themicroservice, to capture one or more metrics regarding the microservice.The agent optimizes the invocation of the method associated with themicroservice. The agent provides the one or more metrics regarding themicroservice to a user interface.

Other embodiments are described below, and this overview is not meant tolimit the scope of the present disclosure.

DESCRIPTION

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. The Internet is an exampleof a WAN that connects disparate networks throughout the world,providing global communication between nodes on various networks. Othertypes of networks, such as field area networks (FANs), neighborhood areanetworks (NANs), personal area networks (PANs), enterprise networks,etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discreteframes or packets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other. Computer networks may be furtherinterconnected by an intermediate network node, such as a router, toextend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or power-line communication networks. That is, in addition toone or more sensors, each sensor device (node) in a sensor network maygenerally be equipped with a radio transceiver or other communicationport, a microcontroller, and an energy source, such as a battery.Generally, size and cost constraints on smart object nodes (e.g.,sensors) result in corresponding constraints on resources such asenergy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices is interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations. Servers 152-154 may include,in various embodiments, any number of suitable servers or othercloud-based resources. As would be appreciated, network 100 may includeany number of local networks, data centers, cloud environments,devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork is topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc. Furthermore, in various embodiments, network 100 mayinclude one or more mesh networks, such as an Internet of Thingsnetwork. Loosely, the term “Internet of Things” or “IoT” refers touniquely identifiable objects (things) and their virtual representationsin a network-based architecture. In particular, the next frontier in theevolution of the Internet is the ability to connect more than justcomputers and communications devices, but rather the ability to connect“objects” in general, such as lights, appliances, vehicles, heating,ventilating, and air-conditioning (HVAC), windows and window shades andblinds, doors, locks, etc. The “Internet of Things” thus generallyrefers to the interconnection of objects (e.g., smart objects), such assensors and actuators, over a computer network (e.g., via IP), which maybe the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, areoften on what is referred to as Low-Power and Lossy Networks (LLNs),which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device(e.g., apparatus) 200 that may be used with one or more embodimentsdescribed herein, e.g., as any of the devices shown in FIGS. 1A-1Babove, and particularly as specific devices as described further below.The device may comprise one or more network interfaces 210 (e.g., wired,wireless, etc.), at least one processor 220, and a memory 240interconnected by a system bus 250, as well as a power supply 260 (e.g.,battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork 100, e.g., providing a data connection between device 200 andthe data network, such as the Internet. The network interfaces may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. For example, interfaces 210 may include wiredtransceivers, wireless transceivers, cellular transceivers, or the like,each to allow device 200 to communicate information to and from a remotecomputing device or server over an appropriate network. The same networkinterfaces 210 also allow communities of multiple devices 200 tointerconnect among themselves, either peer-to-peer, or up and down ahierarchy. Note, further, that the nodes may have two different types ofnetwork connections 210, e.g., wireless and wired/physical connections,and that the view herein is merely for illustration. Also, while thenetwork interface 210 is shown separately from power supply 260, fordevices using powerline communication (PLC) or Power over Ethernet(PoE), the network interface 210 may communicate through the powersupply 260, or may be an integral component of the power supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise one or more functional processes 246, and on certain devices,an illustrative “application monitoring” process 248, as describedherein. Notably, functional processes 246, when executed by processor(s)220, cause each particular device 200 to perform the various functionscorresponding to the particular device's purpose and generalconfiguration. For example, a router would be configured to operate as arouter, a server would be configured to operate as a server, an accesspoint (or gateway) would be configured to operate as an access point (orgateway), a client device would be configured to operate as a clientdevice, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

Application Intelligence Platform

The embodiments herein relate to an application intelligence platformfor application performance management. In one aspect, as discussed withrespect to FIGS. 3-5 below, performance within a networking environmentmay be monitored, specifically by monitoring applications and entities(e.g., transactions, tiers, nodes, and machines) in the networkingenvironment using agents installed at individual machines at theentities. As an example, applications may be configured to run on one ormore machines (e.g., a customer will typically run one or more nodes ona machine, where an application consists of one or more tiers, and atier consists of one or more nodes). The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata may include performance data (e.g., metrics, metadata, etc.) andtopology data (e.g., indicating relationship information). Theagent-collected data may then be provided to one or more servers orcontrollers to analyze the data.

FIG. 3 is a block diagram of an example application intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The application intelligence platform is a system that monitorsand collects metrics of performance data for an application environmentbeing monitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310 and one or moreservers/controllers 320. Note that while FIG. 3 shows four agents (e.g.,Agent 1 through Agent 4) communicatively linked to a single controller,the total number of agents and controllers can vary based on a number offactors including the number of applications monitored, how distributedthe application environment is, the level of monitoring desired, thelevel of user experience desired, and so on.

The controller 320 is the central processing and administration serverfor the application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions (described below) distributed over application servers.Specifically, the controller 320 can receive runtime data from agents310 (and/or other coordinator devices), associate portions of businesstransaction data, communicate with agents to configure collection ofruntime data, and provide performance data and reporting through theinterface 330. The interface 330 may be viewed as a web-based interfaceviewable by a client device 340. In some implementations, a clientdevice 340 can directly communicate with controller 320 to view aninterface for monitoring data. The controller 320 can include avisualization system 350 for displaying the reports and dashboardsrelated to the disclosed technology. In some implementations, thevisualization system 350 can be implemented in a separate machine (e.g.,a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance 320 may be hosted remotely by a provider of theapplication intelligence platform 300. In an illustrative on-premises(On-Prem) implementation, a controller instance 320 may be installedlocally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310 can be implemented as different types of agents with specificmonitoring duties. For example, application agents may be installed oneach server that hosts applications to be monitored. Instrumenting anagent adds an application agent into the runtime process of theapplication.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Database agents query the monitoreddatabases in order to collect metrics and pass those metrics along fordisplay in a metric browser (e.g., for database monitoring and analysiswithin databases pages of the controller's UI 330). Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs(e.g., standalone Java programs) that collect hardware-relatedperformance statistics from the servers (or other suitable devices) inthe monitored environment. The standalone machine agents can be deployedon machines that host application servers, database servers, messagingservers, Web servers, etc. A standalone machine agent has an extensiblearchitecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents andmobile agents to provide performance information from the point of viewof the client, such as a web browser or a mobile native application.Through EUM, web use, mobile use, or combinations thereof (e.g., by realusers or synthetic agents) can be monitored based on the monitoringneeds. Notably, browser agents (e.g., agents 310) can include Reportersthat report monitored data to the controller.

Monitoring through browser agents and mobile agents are generally unlikemonitoring through application agents, database agents, and standalonemachine agents that are on the server. In particular, browser agents maygenerally be embodied as small files using web-based technologies, suchas JavaScript agents injected into each instrumented web page (e.g., asclose to the top as possible) as the web page is served, and areconfigured to collect data. Once the web page has completed loading, thecollected data may be bundled into a beacon and sent to an EUMprocess/cloud for processing and made ready for retrieval by thecontroller. Browser real user monitoring (Browser RUM) provides insightsinto the performance of a web application from the point of view of areal or synthetic end user. For example, Browser RUM can determine howspecific Ajax or iframe calls are slowing down page load time and howserver performance impact end user experience in aggregate or inindividual cases.

A mobile agent, on the other hand, may be a small piece of highlyperformant code that gets added to the source of the mobile application.Mobile RUM provides information on the native mobile application (e.g.,iOS or Android applications) as the end users actually use the mobileapplication. Mobile RUM provides visibility into the functioning of themobile application itself and the mobile application's interaction withthe network used and any server-side applications with which the mobileapplication communicates.

Application Intelligence Monitoring: The disclosed technology canprovide application intelligence data by monitoring an applicationenvironment that includes various services such as web applicationsserved from an application server (e.g., Java virtual machine (JVM),Internet Information Services (IIS), Hypertext Preprocessor (PHP) Webserver, etc.), databases or other data stores, and remote services suchas message queues and caches. The services in the applicationenvironment can interact in various ways to provide a set of cohesiveuser interactions with the application, such as a set of user servicesapplicable to end user customers.

Application Intelligence Modeling: Entities in the applicationenvironment (such as the JBoss service, MQSeries modules, and databases)and the services provided by the entities (such as a login transaction,service or product search, or purchase transaction) may be mapped to anapplication intelligence model. In the application intelligence model, abusiness transaction represents a particular service provided by themonitored environment. For example, in an e-commerce application,particular real-world services can include a user logging in, searchingfor items, or adding items to the cart. In a content portal, particularreal-world services can include user requests for content such assports, business, or entertainment news. In a stock trading application,particular real-world services can include operations such as receivinga stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of theparticular service provided by the monitored environment provides a viewon performance data in the context of the various tiers that participatein processing a particular request. A business transaction, which mayeach be identified by a unique business transaction identification (ID),represents the end-to-end processing path used to fulfill a servicerequest in the monitored environment (e.g., adding items to a shoppingcart, storing information in a database, purchasing an item online,etc.). Thus, a business transaction is a type of user-initiated actionin the monitored environment defined by an entry point and a processingpath across application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request(e.g., a socket call, illustratively associated with the TCP layer). Abusiness transaction can be created by detecting incoming requests at anentry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment (e.g., associating the business transaction with a 4-tupleof a source IP address, source port, destination IP address, anddestination port). A flow map can be generated for a businesstransaction that shows the touch points for the business transaction inthe application environment. In one embodiment, a specific tag may beadded to packets by application specific agents for identifying businesstransactions (e.g., a custom header field attached to a hypertexttransfer protocol (HTTP) payload by an application agent, or by anetwork agent when an application makes a remote socket call), such thatpackets can be examined by network agents to identify the businesstransaction identifier (ID) (e.g., a Globally Unique Identifier (GUID)or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or Common Language Runtime (CLR) onwhich a monitoring Agent is installed. Each node identifies itself inthe application intelligence model. The Agent installed at the node isconfigured to specify the name of the node, tier, and businessapplication under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which, isalong with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier. Similarly, a machine agent cannot belong to more than onetier. However, more than one machine agent can be installed on amachine.

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for system by detecting slow, very slow, andstalled transactions. Configurable thresholds provide a flexible way toassociate the right business context with a slow request to isolate theroot cause.

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected. Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline”. The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. In some implementations, this healthrule can define a warning condition as the combination of an averageresponse time greater than the default baseline by 2 standard deviationsand a load greater than 100 calls per minute. In some implementations,the health rule that tests whether the business transaction error rateis much higher than normal can define a critical condition as thecombination of an error rate greater than the default baseline by 3standard deviations and an error rate greater than 10 errors per minuteand a load greater than 50 calls per minute. In some implementations,this health rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute. These are non-exhaustive and non-limitingexamples of health rules and other health rules can be defined asdesired by the user.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) application programming interface (API) thatreturns either the JavaScript Object Notation (JSON) or the eXtensibleMarkup Language (XML) format. Also, the REST API can be used to queryand manipulate the application environment.

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

FIG. 4 illustrates an example application intelligence platform (system)400 for performing one or more aspects of the techniques herein. Thesystem 400 in FIG. 4 includes client device 405 and 492, mobile device415, network 420, network server 425, application servers 430, 440, 450,and 460, asynchronous network machine 470, data stores 480 and 485,controller 490, and data collection server 495. The controller 490 caninclude visualization system 496 for providing displaying of the reportgenerated for performing the field name recommendations for fieldextraction as disclosed in the present disclosure. In someimplementations, the visualization system 496 can be implemented in aseparate machine (e.g., a server) different from the one hosting thecontroller 490.

Client device 405 may include network browser 410 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection 495, controller 490, or another device.Agent 412 may perform other operations related to monitoring a requestor a network at client 405 as discussed herein including reportgenerating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or other portable device. Both client device 405 and mobiledevice 415 may include hardware and/or software configured to access aweb service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may report data todata collection server 495 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 4).Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.,“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4.

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorcode running in a virtual machine 432 (or other program language, suchas a PHP, .Net, or C program), machine resources, network layer data,and communicate with byte instrumented code on application server 430and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a Java agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier of nodes, or other entity.A node may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agents may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from one or morenetwork interfaces (e.g., so that data generated/received by all theapplications using sockets can be intercepted). The network agent mayreceive flow data from a language agent that is associated withapplications to be monitored. For flows in the flow group data thatmatch flow data provided by the language agent, the network agent rollsup the flow data to determine metrics such as TCP throughput, TCP loss,latency, and bandwidth. The network agent may then report the metrics,flow group data, and call chain data to a controller. The network agentmay also make system calls at an application server to determine systeminformation, such as for example a host status check, a network statuscheck, socket status, and other information.

A machine agent, which may be referred to as an infrastructure agent,may reside on the host and collect information regarding the machinewhich implements the host. A machine agent may collect and generatemetrics from information such as processor usage, memory usage, andother hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or 415associated with a user or another source). The request identifier may besent to client 405 or mobile device 415, whichever device sent therequest. In embodiments, the request identifier may be created when datais collected and analyzed for a particular business transaction.

Each of application servers 440, 450, and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452, and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454, and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452, and462 may communicate with each other as part of performing a distributedtransaction. Each application may call any application or method ofanother virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 460. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 495. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444, and 454 (also referred to hereinas “application monitoring agents”). Controller 490 may associateportions of business transaction data, communicate with agents toconfigure collection of data, and provide performance data and reportingthrough an interface. The interface may be viewed as a web-basedinterface viewable by client device 492, which may be a mobile device,client device, or any other platform for viewing an interface providedby controller 490. In some embodiments, a client device 492 may directlycommunicate with controller 490 to view an interface for monitoringdata.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, workstation or othercomputing device. Client computer 492 may communicate with controller490 to create and view a custom interface. In some embodiments,controller 490 provides an interface for creating and viewing the custominterface as a content page, e.g., a web page, which may be provided toand rendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology, which is a specific implementation of device 200 ofFIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts ofthe likes of clients 405, 492, network server 425, servers 430, 440,450, 460, asynchronous network machine 470, and controller 490 of FIG.4. (Note that the specifically configured system 500 of FIG. 5 and thecustomized device 200 of FIG. 2 are not meant to be mutually exclusive,and the techniques herein may be performed by any suitably configuredcomputing device.)

The computing system 500 of FIG. 5 includes one or more processors 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor 510. Main memory 520 can store the executablecode when in operation. The system 500 of FIG. 5 further includes a massstorage device 530, portable storage medium drive(s) 540, output devices550, user input devices 560, a graphics display 570, and peripheraldevices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable or remote storagedevice 540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present disclosure for purposes of loading thatsoftware into main memory 520.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a compact disk, digital video disk,magnetic disk, flash storage, etc. to input and output data and code toand from the computer system 500 of FIG. 5. The system software forimplementing embodiments of the present disclosure may be stored on sucha portable medium and input to the computer system 500 via the portablestorage device 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, handheld computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

Microservice Manager and Optimizer

As noted above, microservices have emerged as a major paradigm shift inthe computing world. Rather than deploying monolithic applications, manycompanies are now choosing instead to develop scalable microservicesthat break the tasks of the application into individual runtimes, tohandle the web service calls. In the context of the Java programminglanguage, there are now multiple frameworks designed to supportmicroservices. To do so, these platforms typically operate by exposingRepresentational State Transfer (REST) endpoints, which perform specificservices. Microservices also typically use runtimes that are muchsmaller in terms of memory and much more dynamic than monolithicapplications. Consequently, traditional APM solutions are not wellsuited for microservice frameworks, as their additional overhead may notbe compatible with the resource constraints found in microserviceframeworks.

The techniques herein, therefore, allow for APM solutions to be extendedto microservice frameworks. In some aspects, the techniques hereinintroduce a lightweight and direct management solution for microserviceframeworks that are also able to optimize the runtime for maximumperformance and reliability.

Specifically, according to one or more embodiments described herein, anagent executed by a device detects an invocation that is made using Javareflection of a method associated with a microservice. The agentinstruments the invocation of the method associated with themicroservice, to capture one or more metrics regarding the microservice.The agent optimizes the invocation of the method associated with themicroservice. The agent provides the one or more metrics regarding themicroservice to a user interface.

Operationally, FIG. 6 illustrates a simplified architecture for amicroservice framework with an agent, according to various embodiments.As shown, framework 600 may include the following components: a client602, an API gateway 604, and n-number of microservices 606. Examplemicroservice frameworks that follow this architecture include SpringBoot, DropWizard, Swagger, RestEasy, among others.

During operation, client 602, such as a mobile device, personalcomputer, or other networked device may send messages to API gateway604, such as requests for information, command, etc. Typically, suchmessaging may take the form of HTTP(S) requests or responses that areprocessed by API gateway 604. For instance, client 602 may issue a RESTrequest to API gateway 604 via a web browser or other app.

In general, API gateway 604 facilitates client 602 interacting with theapplication that comprises microservices 606 a-606 n. More specifically,in response to a message from client 602, API gateway 604 may call thecorresponding microservice among microservices 606 a-606 n, such as byusing REST. For instance, assume that the application that comprisesmicroservices 606 a-606 n is an online retail application. In such acase, one microservice 606 may be a search service that allows client602 to perform an inventory search, another microservice 606 may be ashopping cart service, a further microservice 606 may be a billingservice, etc.

In the case of a Java application, a key feature of Java is reflection,which allows fields, interfaces, classes, and methods to be inspected atruntime. The benefit of this is that their names do not need to be knownat compile time. In addition, methods can be invoked, new objects can beinstantiated, and field values can be get/set, all using reflection.Accordingly, many Java-based applications that make use of microservicesrely on Java reflection to invoke REST.

Unfortunately, Java reflection can have a significant impact on resourceconsumption, particularly as it relates to smaller JVMs as found inmicroservices. Notably, using reflection to invoke a method in a class(java.lang.reflect.Method.invoke) delegates this call to what is calleda MethodAccessor. This MethodAccessor determines which class willultimately make this call. The default MethodAccessor is a Java NativeInterface (JNI) Call class (native), which is fairly slow.

To address the slowness of native reflection in Java, special JVMparameters exist which basically will change MethodAccessor to a muchbetter performing Java implementation, once the number of invocationsgets to a certain count. The difference is on the order approximatelytwenty times faster. However, this also comes at a big price in terms ofmemory, both PermGen and I-leap. The Java implementation creates oneclass (sunseflect.GeneratedMethodAccessor) per MethodAccessor, whichcauses new classes to be rapidly added that affect PermGen space (nativememory) where classes are stored. In addition, there is an impact onheap space. This process is known as “inflation” and can be somewhatcontrolled via properties such as sunseflectinflationThreshold andsunseflect.noInflation. However, inflation cannot be is stoppedentirely, nor is there a way to pick and choose which and how many“inflations” take place. In other words, the approach taken today isbrute force and lacks intelligence as to whether inflation should takeplace or to distinguish between classes that should be inflated andthose that should not.

To illustrate the invocation of REST via reflection in a typicalframework, the following call stack was captured during a single RESTcall in Spring Boot. Note that the JNI Native call comes at the end andis calling the “slow” native reflection method. In certain embodiments,this may be upgraded to the faster, “direct” reflection method.

-   sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java)//Here    is the “slow” JNI call-   sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.    java:43) . // Here is the Delegate-   java.lang.reflect.Method.invoke(Method.java:498). //Here is the    standard Reflection Method Call-   org.glassfish.jersey.server.model.internal.ResourceMethodInvocationHandlerFactory.lam    bda$static$0(ResourceMethodInvocationHandlerFactory.java:76)    org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher$1.run(AbstractJavaResourceMethodDispatcher.java:148)    org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.invok    e(AbstractJavaResourceMethodDispatcher.java:191)    org.glassfish.jersey.server.model.internal.JavaResourceMethodDispatcherProvider$Type    OutInvoker.doDispatch(JavaResourceMethodDispatcherProvider.java:243)    org.glassfish.jersey.server.model.internal.AbstractJavaResourceMethodDispatcher.dispat    ch(AbstractJavaResourceMethodDispatcher.java:103)    org.glassfish.jersey.server.model.ResourceMethodInvoker.invoke(ResourceMethodInvok    er.java:493)    org.glassfish.jersey.server.model.ResourceMethodInvoker.apply(ResourceMethodInvoke    r.java:415)    org.glassfish.jersey.server.model.ResourceMethodInvoker.apply(ResourceMethodInvoke    rjava:104)    org.glassfish.jersey.server.ServerRuntime$1.run(ServerRuntime.java:277)    org.glassfish.jersey.internal.Errors$1.call(Errors.java:272)    org.glassfish.jersey.internal.Errors$1.call(Errors.java:268)    org.glassfish.jersey.internal.Errors.process(Errors.java:316)    org.glassfish.jersey.internal.Errors.process(Errors.java:298)    org.glassfish.jersey.internal.Errors.process(Errors.java:268)    org.glassfish.jersey.process.internal.RequestScope.runInScope(RequestScope.java:289)    org.glassfish.jersey.server.ServerRuntime.process(ServerRuntime.java:256)    org.glassfish.jersey.server.ApplicationHandler.handle(ApplicationHandler.java:703)    org.glassfish.jersey.servlet.WebComponent.serviceImpl(WebComponent.java:416)    org.glassfish.jersey.servlet.WebComponent.service(WebComponent.java:370)    org.glassfish.jersey.servlet.ServletContainer.service(ServletContainer.java:389)    org.glassfish.jersey.servlet.ServletContainer.service(ServletContainer.java:342)    org.glassfish.jersey.servlet.ServletContainer.service(ServletContainer.java:229)    org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.    java:231)    org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:166)    org.apache.tomcat.webocket.server.WsFilter.doFilter(WsFilter.java:53)    org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.    java:193)    org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:166)    org.springframework.web.filter.RequestContextFilter.doFilterInternal(RequestContextFil    ter.java:99)    org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.jav    a:107)    org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.    java:193)    org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:166)    org.springframework.web.filter.CharacterEncodingFilter.doFilterInternal(CharacterEnco    dingFilter.java:200)    org.springframework.web.filter.OncePerRequestFilter.doFilter(OncePerRequestFilter.jav    a:107)    org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.    java:193)    org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:166)    org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:199)    org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:96)    org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:490)    org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:139)    org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:92)    org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:74)    org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:343)    org.apache.coyote.http11.Http11Processor.service(Http11Processor.java:408)    org.apache.coyote.AbstractProcessorLight.process(AbstractProcessorLight.java:66)    org.apache.coyote.AbstractProtocol$ConnectionHandler.process(AbstractProtocol.java:7 70)    org.apache.tomcat.util.net.NioEndpoint$SocketProcessor.doRun(NioEndpoint.java:1 415)    org.apache.tomcat.util.net.SocketProcessorBase.run(SocketProcessorBase.java:49)    java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)    java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)    org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61)    javalang.Thread.run(Thread.java:745)

According to various embodiments, the techniques herein introduce anagent 608 (e.g., an APM agent) that may be executed within microserviceframework 600, such as by API gateway 604 and/or microservices 606 a-606n. In some embodiments, as detailed below, agent 608 may function toinstrument the REST calls made within framework 600, such as at theinvocation of the call. In further embodiments, agent 608 may also beconfigured to optimize these invocations, so as to control whichreflection calls are allowed to be “inflated.” Even for those reflectioncalls that are not allowed to be inflated, agent 608 may still optimizethe invocation using a custom MethodHandles that takes advantage ofJava's dynamic invoke instruction, which is still approximately threetimes faster than that of the native reflection call and will notconsume memory like direct reflection does.

More specifically, in Java 7, Oracle introduced a new instruction, thefirst since Java's creation, called “invokedynamic.” This instruction isa game changer with respect to performance and benchmarks show that itprovides performance on par with direct method calls, when usedproperly. Unfortunately, reflection used in microservice frameworkstoday does not make use of this new instruction, which opt to usetraditional reflection, instead. Thus, using the MethodHandles class inplace of the standard Method inflation can perform up to five timesfaster than that of the standard JNI Native MethodAccessor.

According to various embodiments, agent 608 may identify reflectioninvocations for REST endpoints and apply the following optimizations:

-   -   If the reflection invocation does not have priority, agent 608        may ensure that the MethodHandle call is made, instead of the        JNI native call. This results in a speed enhancement of        approximately three to five times that of the native call.    -   If the reflection invocation does have priority, agent 608 may        ensure that it uses the direct reflection call. This results in        a speed enhancement of approximately twenty times that of the        native call, but at the cost of greater memory consumption.

-   The key to the above optimization is that agent 608 now controls how    many reflection inflations occur and which classes uses them. Even    for those that are not inflated, the above approach still replaces    the native reflection approach with MethodHandle calls, still    resulting in a speed increase. This is the best of both worlds using    a much faster default, automatically setting the REST endpoints and    designed packages to the maximum priority and limit how many of    these classes can be generated, so that running out of memory does    not become an issue.

In one embodiment, agent 608 may use machine learning to optimize theefficiency of the reflection calls and prevent many bottlenecks due toreflection inflation. Indeed, machine learning can be used to selectwhich reflection calls are allowed to be inflated using directreflection. In general, machine learning is concerned with the designand the development of techniques that receive empirical data as input(e.g., telemetry data regarding traffic in the network) and recognizecomplex patterns in the input data. For example, some machine learningtechniques use an underlying model M, whose parameters are optimized forminimizing the cost function associated to M, given the input data. Forinstance, in the context of classification, the model M may be astraight line that separates the data into two classes (e.g., labels)such that M=a*x+b*y+c and the cost function is a function of the numberof misclassified points. The learning process then operates by adjustingthe parameters a,b,c such that the number of misclassified points isminimal. After this optimization/learning phase, the model M can be usedto evaluate new data points, such as new reflection calls within theapplication. Often, M is a statistical model, and the cost function isinversely proportional to the likelihood of M, given the input data.

Example machine learning techniques that agent 608 can employ to controlwhich reflection call is made may include, but are not limited to,nearest neighbor (NN) techniques (e.g., k-NN models, replicator NNmodels, etc.), statistical techniques (e.g., Bayesian networks, etc.),clustering techniques (e.g., k-means, mean-shift, etc.), neural tonetworks (e.g., reservoir networks, artificial neural networks, etc.),support vector machines (SVMs), logistic or other regression, Markovmodels or chains, principal component analysis (PCA) (e.g., for linearmodels), multi-layer perceptron (MLP) artificial neural networks (ANNs)(e.g., for non-linear models), replicating reservoir networks (e.g., fornon-linear models, typically for time series), random forestclassification, or the like.

In addition to optimizing the invocation of a particular method, agent608 may also instrument the invocation, to capture one or more metricsregarding the corresponding microservice 606. Note that REST endpointsare typically invoked in a microservice framework using the sameclass/method calls for all endpoints: java.lang.Method.invoke. Thus, insome embodiments, agent 608 may instrument java.lang.Method.invoke, tocapture the one or more metrics. Doing so allows agent 608 to instrumentonly a single method. This is in contrast to traditional APM agents thattypically instrument the actual REST endpoint methods, resulting inevery single REST endpoint requiring instrumentation and resulting inmore memory consumption.

As would be appreciated, java.lang.Method.invoke may also be used tomake non-REST calls, as well. To differentiate between REST calls andnon-REST calls, agent 608 may scan the classes as loaded for the RESTendpoint annotation and storing the names. Then, when a reflection calltakes place, agent 608 can use the stored this to determine whether aREST endpoint is being invoked. If so, agent 608 may instrument theinvocation, in addition to optimizing the reflection that it uses.

By intercepting the inbound call to the “invoke” method, agent 608 caninstrument the invocation and optimize it by executing the following,according to the above optimizations:

-   -   If it is a non-priority package, execute MethodHandle.invoke( .        . . ) instead of the native NativeMethodAccessorImpl.invoke0( .        . . ) call.    -   If it is a priority package, execute the NativeMethodAccessor        logic, but with a threshold of zero set, so that it will        immediately create the Java MethodAccessor call on the very        first usage.

As would be appreciated, most APM agents are designed to cache and sendmetrics at periodic intervals to a backend. There are various reasonswhy this is done—one of the most important is that it's difficult toconnect into an agent on an application because of firewalls. However,in a typical microservice environment, there is little room foradditional third-party libraries used to send metrics because themicroservice manager does not use third party libraries at all. Instead,the network connections inbound, and outbound use “built in” JDK classesthat are already loaded. Accordingly, in some embodiments, remote accessto agent 608 may be achieved from outside the site using a techniquewhere the agent connects to a gateway proxy via WebSocket and the useris able to access a “light” HTML web server on agent 608 itself fordiagnostics and metrics.

A prototype agent was created, to illustrate the efficacy of thetechniques herein. FIG. 7 illustrates an example folder structure 700for the prototype agent, in some embodiments. As shown, the agent can beattached in one of two ways:

-   -   Dynamic Attach (on demand): /attach-micro-manager.sh PID    -   Static Attach (in the startup script):        adding-javaagent:micro-manager/lib/microManagerAgentEntry.jar

FIGS. 8A-8D illustrate examples output of the prototype implementing thetechniques herein. More specifically, FIG. 8A illustrates an example 800of the execution of a REST endpoint with a ten second wait. For theprototype, the prototype agent was loaded during startup of Spring Boot.Then, an error was created, as shown in example 810 in FIG. 8B.

FIG. 8C illustrates an example 820 of the information captured by theprototype agent. Notably, the endpoint is “hello.RestComponents.perform”and two calls are made, one of which is an error (“A Rest Error hasoccurred”). The calls allocated 122,400 bytes and 5.3 ms of latency wasobserved over ten seconds. Note that the wait time is almost identicalto the latency, so the time was spent waiting and not working. Lookingat the sampling, the time spent waiting was in a Thread.sleep call as itwas the current method 189 times (almost all of the time). TheEndpointStackTrace was also captured.

Interestingly, there were 270 reflection classes that were optimized touse MethodHandles “dynamicinvoke” versus the native JVM, which appearedin the log as is follows:

-   -   Mon Feb. 17 16:27:12 CST 2020: Discovered Native Reflection        instance (270) sun.reflect.NativeMethodAccessorlmpl@1457093 for        method public abstract long        com.sun.management.ThreadMXBean.getThreadAllocatedBytes(long)

FIG. 8D illustrates an example 830 of the memory usage by the overallJVM. As far as memory consumption, the prototype agent only added 10 mbto the JVM.

In closing, example simplified procedure for instrumenting andoptimizing a microservice-based application, in accordance with one ormore embodiments described herein. For example, a non-generic,specifically configured device (e.g., device 200) may perform procedure900 by executing stored instructions (e.g., process 248, such as amonitoring process) that includes an agent configured to manage andoptimize microservice calls in a microservice framework. As shown,procedure 900 starts at step 905 and continues on to step 910 where, asdescribed in greater detail above, the agent may detect an invocationthat is made using Java reflection of a method associated with amicroservice. For instance, the agent may detect the use ofjava.lang.Method.invoke or another call that relies on Java reflection.

At step 915, as detailed above, the agent may instrument the invocationof the method associated with the microservice, to capture one or moremetrics regarding the microservice. For instance, the agent mayinstrument javalang.Method.invoke or the other invocation of the methodassociated with the microservice, to capture one or more metrics. Suchmetrics may be indicative of a resource consumption by the microservice,a response time of the microservice, an availability of themicroservice, combinations thereof, or the like. To avoid instrumentingcertain calls that also use this type of invocation, such as non-RESTcalls, the agent may differentiate these calls by scanning the classesas loaded.

At step 920, the agent may optimize the invocation of the methodassociated with the microservice, as described in greater detail above.In various embodiments, the agent may do so by evaluating whether thereflection call is attempting to use native reflection calls and/or apriority associated with the package. For instance, the agent mayreplace a NativeMethodAccessorImpl.invoke0( . . . ) call with a JavaMethodAccessor call, if the package has priority, in some embodiments.In a further embodiment, the agent may replace such a native call with aMethodHandle.invoke( . . . ) call for a non-priority package, which willstill result in fast execution without an increase in memoryconsumption.

At step 925, as detailed above, the agent may provide the one or moremetrics regarding the microservice to a user interface. In some cases,the agent may send the metrics to a backend system for forwarding to theuser interface. In other cases, the agent itself may include alightweight HTML web server and provide the metric(s) to a display orother user interface of a client operated by an interested party.

The simplified procedure 900 may then end in step 930, notably with theability to continue ingesting and processing data. Other steps may alsobe included generally within procedure 900.

It should be noted that while certain steps within procedure 900 may beoptional as described above, the steps shown in FIG. 9 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, provide for a microservicemanager and optimizer. In particular, as noted above the techniquesherein introduce a microservice manager and optimizer that can allow forAPM solutions to be extended to microservice frameworks, introducing alightweight and direct management solution for microservice frameworksthat are also able to optimize the runtime for maximum performance andreliability.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative application is monitoring process 248, or another Javaagent, which may include computer executable instructions executed bythe processor 220 to perform functions relating to the techniquesdescribed herein, e.g., in conjunction with corresponding processes ofother devices in the computer network as described herein (e.g., onnetwork agents, controllers, computing devices, servers, etc.).

According to the embodiments herein, a method herein may comprise:detecting, by an agent executed by a device, an invocation that is madeusing Java reflection of a method associated with a microservice;instrumenting, by the agent, the invocation of the method associatedwith the microservice, to capture one or more metrics regarding themicroservice; optimizing, by the agent, the invocation of the methodassociated with the microservice; and providing, by the agent, the oneor more metrics regarding the microservice to a user interface.

In one embodiment, the method associated with the microservice isRepresentational State Transfer (REST)-based. In another embodiment,detecting the invocation of the method associated with the microservicecomprises determining whether the method is a REST method, by evaluatinga class associated with the method associated with the microservice. Ina further embodiment, optimizing the invocation of the method associatedwith the microservice comprises selecting a reflection call for theinvocation, based on a priority associated with the method associatedwith the microservice. In yet another embodiment, the reflection callselected for the invocation is a dynamic invoke call, and optimizing theinvocation of the method associated with the microservice furthercomprises replacing a native reflection call of the invocation with thedynamic invoke call. In another embodiment, the reflection call for theinvocation is selected using a machine learning model trained to reduceresource consumption by the invocation. In an additional embodiment,optimizing the invocation of the method associated with the microservicecomprises preventing the invocation of the method associated with themicroservice from using a direct reflection call. In a furtherembodiment, the one or more metrics regarding the microservice isprovided to a user interface via a webserver of the agent. In anotherembodiment, the one or more metrics associated with the microservice areindicative of at least one of: an availability of the microservice or aresponse time of the microservice.

According to the embodiments herein, a tangible, non-transitory,computer-readable medium storing program instructions that cause adevice to execute a process comprising: detecting, by an agent executedby the device, an invocation that is made using Java reflection of amethod associated with a microservice; instrumenting, by the agent, theinvocation of the method associated with the microservice, to captureone or more metrics regarding the microservice; optimizing, by theagent, the invocation of the method associated with the microservice;and providing, by the agent, the one or more metrics regarding themicroservice to a user interface.

Further, according to the embodiments herein an apparatus is disclosedcomprising: one or more network interfaces; a processor coupled to theone or more network interfaces and configured to execute one or moreprocesses; and a memory configured to store a process that is executableby the processor, the process when executed configured to: detect aninvocation that is made using Java reflection of a method associatedwith a microservice; instrument the invocation of the method associatedwith the microservice, to capture one or more metrics regarding themicroservice; optimize the invocation of the method associated with themicroservice; and provide the one or more metrics regarding themicroservice to a user interface.

While there have been shown and described illustrative embodimentsabove, it is to be understood that various other adaptations andmodifications may be made within the scope of the embodiments herein.For example, while certain embodiments are described herein with respectto certain types of networks in particular, the techniques are notlimited as such and may be used with any computer network, generally, inother embodiments. Moreover, while specific technologies, protocols, andassociated devices have been shown, such as Java, TCP, IP, and so on,other suitable technologies, protocols, and associated devices may beused in accordance with the techniques described above. In addition,while certain devices are shown, and with certain functionality beingperformed on certain devices, other suitable devices and processlocations may be used, accordingly. That is, the embodiments have beenshown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyembodiment or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularembodiments. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a server” or “by acontroller,” those skilled in the art will appreciate that agents of theapplication intelligence platform (e.g., application agents, networkagents, language agents, etc.) may be considered to be extensions of theserver (or controller) operation, and as such, any process stepperformed “by a server” need not be limited to local processing on aspecific server device, unless otherwise specifically noted as such.Furthermore, while certain aspects are described as being performed “byan agent” or by particular types of agents (e.g., application agents,network agents, etc.), the techniques may be generally applied to anysuitable software/hardware configuration (libraries, modules, etc.) aspart of an apparatus or otherwise.

Similarly, while operations are depicted in the drawings in a particularorder, this is should not be understood as requiring that suchoperations be performed in the particular order shown or in sequentialorder, or that all illustrated operations be performed, to achievedesirable results. Moreover, the separation of various system componentsin the embodiments described in the present disclosure should not beunderstood as requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true intent and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: detecting, by an agentexecuted by a device, an invocation that is made using Java reflectionof a method associated with a microservice; instrumenting, by the agent,the invocation of the method associated with the microservice, tocapture one or more metrics regarding the microservice; optimizing, bythe agent, the invocation of the method associated with the microserviceby selecting a reflection call among a plurality of reflection calls forthe invocation of the method associated with the microservice based on apriority associated with the method associated with the microservice;and providing, by the agent, the one or more metrics regarding themicroservice to a user interface.
 2. The method as in claim 1, whereinthe method associated with the microservice is Representational StateTransfer (REST)-based.
 3. The method as in claim 2, wherein detectingthe invocation of the method associated with the microservice comprises:determining whether the method is a REST method, by evaluating a classassociated with the method associated with the microservice.
 4. Themethod as in claim 1, wherein the reflection call selected for theinvocation is a dynamic invoke call, and wherein optimizing theinvocation of the method associated with the microservice furthercomprises: replacing a native reflection call of the invocation with thedynamic invoke call.
 5. The method as in claim 1, wherein the reflectioncall for the invocation is selected using a machine learning modeltrained to reduce resource consumption by the invocation.
 6. The methodas in claim 1, wherein optimizing the invocation of the methodassociated with the microservice comprises: preventing the invocation ofthe method associated with the microservice from using a directreflection call.
 7. The method as in claim 1, wherein the one or moremetrics regarding the microservice is provided to a user interface via awebserver of the agent.
 8. The method as in claim 1, wherein the one ormore metrics associated with the microservice are indicative of at leastone of: an availability of the microservice or a response time of themicroservice.
 9. A tangible, non-transitory, computer-readable mediumstoring program instructions that cause a device to execute a processcomprising: detecting, by an agent executed by the device, an invocationthat is made using Java reflection of a method associated with amicroservice; instrumenting, by the agent, the invocation of the methodassociated with the microservice, to capture one or more metricsregarding the microservice; optimizing, by the agent, the invocation ofthe method associated with the microservice by selecting a reflectioncall among a plurality of reflection calls for the invocation of themethod associated with the microservice based on a priority associatedwith the method associated with the microservice; and providing, by theagent, the one or more metrics regarding the microservice to a userinterface.
 10. The computer-readable medium as in claim 9, wherein themethod associated with the microservice is Representational StateTransfer (REST)-based.
 11. The computer-readable medium as in claim 10,wherein detecting the invocation of the method associated with themicroservice comprises: determining whether the method associated withthe microservice is a REST method, by evaluating a class associated withthe method.
 12. The computer-readable medium as in claim 9, wherein thereflection call for the invocation is selected using a machine learningmodel trained to reduce resource consumption by the invocation.
 13. Thecomputer-readable medium as in claim 9, wherein the reflection callselected for the invocation is a dynamic invoke call, and whereinoptimizing the invocation of the method associated with the microservicefurther comprises: replacing a native reflection call of the invocationwith the dynamic invoke call.
 14. The computer-readable medium as inclaim 9, wherein optimizing the invocation of the method comprises:preventing the invocation of the method associated with the microservicefrom using a direct reflection call.
 15. The computer-readable medium asin claim 9, wherein the one or more metrics regarding the microserviceassociated with the microservice is provided to the user interface via awebserver of the agent.
 16. The computer-readable medium as in claim 9,wherein the one or more metrics are indicative of at least one of: anavailability of the microservice or a response time of the microservice.17. An apparatus, comprising: one or more network interfaces; aprocessor coupled to the one or more network interfaces and configuredto execute one or more processes; and a memory configured to store aprocess that is executable by the processor, the process when executedconfigured to: detect an invocation that is made using Java reflectionof a method associated with a microservice; instrument the invocation ofthe method associated with the microservice, to capture one or moremetrics regarding the microservice; optimize the invocation of themethod associated with the microservice by selecting a reflection callamong a plurality of reflection calls for the invocation of the methodassociated with the microservice based on a priority associated with themethod associated with the microservice; and provide the one or moremetrics regarding the microservice to a user interface.
 18. Theapparatus as in claim 17, wherein the method associated with themicroservice is Representational State Transfer (REST)-based.